SoroushVahidi/scidocs huggingface dataset:
Artificial Intelligence in the NASA Volcano Sensorweb : Over a Decade in Operations
(2015)

Volcanic activity can occur with little or no warning. Increasing numbers of space borne assets can enable coordinated measurements of volcanic events to enhance both scientific study and hazard response. We describe the use of space and ground measurements to target further measurements as part of a worldwide volcano monitoring system. We utilize a number of alert systems including the MODVOLC, GOESVOLC, US Air Force Weather Advisory, and Volcanic Ash Advisory Center (VAAC) alert systems. Additionally we use in-situ data from ground instrumentation at a number of volcanic sites, including Iceland. Artificial Intelligence Software plays a key role in the Volcano Sensorweb. First, several in-situ volcano monitoring networks use “intelligent” data interpretation software to trigger alerts that can then be used to allocate network resources, notify human agents, and even task space observations. Second, the Earth Observing One (EO-1) spacecraft uses Artificial Intelligence Software to automatically task the spacecraft to execute observations. Third, EO-1 also interprets thermal data onboard to allow for faster notifications of volcanic activity. Finally some data interpretation steps use intelligent software such as Random Decision Forest Methods used to automatically estimate volcanic plume heights in Worldview-2 Imagery.
Comprehensive Database for Facial Expression Analysis
(2000)

Within the past decade, significant effort has occurred in developing methods of facial expression analysis. Because most investigators have used relatively limited data sets, the generalizability of these various methods remains unknown. We describe the problem space for facial expression analysis, which includes level of description, transitions among expression, eliciting conditions, reliability and validity of training and test data, individual differences in subjects, head orientation and scene complexity, image characteristics, and relation to non-verbal behavior. We then present the CMU-Pittsburgh AU-Coded Face Expression Image Database, which currently includes 2105 digitized image sequences from 182 adult subjects of varying ethnicity, performing multiple tokens of most primary FACS action units. This database is the most comprehensive test-bed to date for comparative studies of facial expression
Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction.
(2003)

Computer animated agents and robots bring a social dimension to human computer interaction and force us to think in new ways about how computers could be used in daily life. Face to face communication is a real-time process operating at a a time scale in the order of 40 milliseconds. The level of uncertainty at this time scale is considerable, making it necessary for humans and machines to rely on sensory rich perceptual primitives rather than slow symbolic inference processes. In this paper we present progress on one such perceptual primitive. The system automatically detects frontal faces in the video stream and codes them with respect to 7 dimensions in real time: neutral, anger, disgust, fear, joy, sadness, surprise. The face finder employs a cascade of feature detectors trained with boosting techniques [15, 2]. The expression recognizer receives image patches located by the face detector. A Gabor representation of the patch is formed and then processed by a bank of SVM classifiers. A novel combination of Adaboost and SVM's enhances performance. The system was tested on the Cohn-Kanade dataset of posed facial expressions [6]. The generalization performance to new subjects for a 7- way forced choice correct. Most interestingly the outputs of the classifier change smoothly as a function of time, providing a potentially valuable representation to code facial expression dynamics in a fully automatic and unobtrusive manner. The system has been deployed on a wide variety of platforms including Sony's Aibo pet robot, ATR's RoboVie, and CU animator, and is currently being evaluated for applications including automatic reading tutors, assessment of human-robot interaction.
Toward an affect-sensitive multimodal human-computer interaction
(2003)

The ability to recognize affective states of a person we are communicating with is the core of emotional intelligence. Emotional intelligenceisafacetofhumanintelligencethathasbeenarguedtobe indispensable and perhaps the most important for successful interpersonal social interaction. This paper argues that next-generation human–computer interaction (HCI) designs need to include the essence of emotional intelligence—the ability to recognize a user’s affective states—in order to become more human-like, more effective, and more efficient. Affective arousal modulates all nonverbal communicative cues (facial expressions, body movements, and vocal and physiological reactions). In a face-to-face interaction, humans detect and interpret those interactive signals of their communicator with little or no effort. Yet design and development of an automated system that accomplishes these tasks is rather difficult. This paper surveys the past work in solving these problems by a computer and provides a set of recommendations for developing the first part of an intelligent multimodal HCI—an automatic personalized analyzer of a user’s nonverbal affective feedback.
Parametric Hidden Markov Models for Gesture Recognition
(1999)

ÐA new method for the representation, recognition, and interpretation of parameterized gesture is presented. By parameterized gesture we mean gestures that exhibit a systematic spatial variation; one example is a point gesture where the relevant parameter is the two-dimensional direction. Our approach is to extend the standard hidden Markov model method of gesture recognition by including a global parametric variation in the output probabilities of the HMM states. Using a linear model of dependence, we formulate an expectation-maximization (EM) method for training the parametric HMM. During testing, a similar EM algorithm simultaneously maximizes the output likelihood of the PHMM for the given sequence and estimates the quantifying parameters. Using visually derived and directly measured three-dimensional hand position measurements as input, we present results that demonstrate the recognition superiority of the PHMM over standard HMM techniques, as well as greater robustness in parameter estimation with respect to noise in the input features. Last, we extend the PHMM to handle arbitrary smooth (nonlinear) dependencies. The nonlinear formulation requires the use of a generalized expectation-maximization (GEM) algorithm for both training and the simultaneous recognition of the gesture and estimation of the value of the parameter. We present results on a pointing gesture, where the nonlinear approach permits the natural spherical coordinate parameterization of pointing direction. Index TermsÐGesture recognition, hidden Markov models, expectation-maximization algorithm, time-series modeling, computer vision.
A high-efficient LLCC series-parallel resonant converter
(2010)

A high efficient LLCC-type resonant dc-dc converter is discussed in this paper for a low-power photovoltaic application. Emphasis is put on the different design mechanisms of the resonant tank. At the same time soft switching of the inverter as well as the rectifier bridge are regarded. Concerning the design rules, a new challenge is solved in designing a LLCC-converter with voltage-source output. Instead of the resonant elements, ratios of them, e.g. the ratio of inductances Ls/Lp is considered as design parameters first. Furthermore, the derived design rule for the transformer-inductor device fits directly into the overall LLCC-design. Due to the nature of transformers, i.e. the relation of the inductances Ls/Lp is only a function of geometry, this design parameter is directly considered by geometry. Experimental results demonstrate the high efficiency.
A dynamic model for integrating simple web spam classification techniques
(2015)

Over the last years, Internet spam content has spread enormously inside web sites mainly due to the emergence of new web technologies oriented towards the online sharing of resources and information. In such a situation, both academia and industry have shown their concern to accurately detect and effectively control web spam, resulting in a good number of anti-spam techniques currently available. However, the successful integration of different algorithms for web spam classification is still a challenge. In this context, the present study introduces WSF2, a novel web spam filtering framework specifically designed to take advantage of multiple classification schemes and algorithms. In detail, our approach encodes the life cycle of a case-based reasoning system, being able to use appropriate knowledge and dynamically adjust different parameters to ensure continuous improvement in filtering precision with the passage of time. In order to correctly evaluate the effectiveness of the dynamic model, we designed a set of experiments involving a publicly available corpus, as well as different simple well-known classifiers and ensemble approaches. The results revealed that WSF2 performed well, being able to take advantage of each classifier and to achieve a better performance when compared to other alternatives. WSF2 is an open-source project licensed under the terms of the LGPL publicly available at https://sourceforge.net/
Broadband compact microstrip patch antenna design loaded by multiple split ring resonator superstrate and substrate
(2016)

We present microstrip patch antenna loaded with multiple split ring resonator substrate and superstrate. We analyze how the loading of split ring resonator superstrate and substrate can improve the bandwidth compared to the simple microstrip patch antenna and microstrip patch antenna loaded with split ring resonator superstrate. Another important observation is made for multiple split ring resonator loading in superstrate and substrate of microstrip patch antenna. The design is compared for two, three, and four-ring split ring resonator loading. The designs are also compared for different gap spacing between the rings. All three designs are compared for small gap and large gap between the rings. The design results in the form of reflection coefficient and bandwidth is presented in this manuscript. The design results are also compared with previously published designs.
Intentions to use social media in organizing and taking vacation trips
(2011)

0747-5632/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.chb.2010.05.022 * Corresponding author. Tel.: +34 922317075. E-mail addresses: eparra@ull.es (E. Parra-López), Bulchand-Gidumal), dgtano@ull.es (D. Gutiérrez-Ta Armas). This work proposes a theoretical model to explain the factors determining the intentions to use social media when organizing and taking vacation trips. Understanding the antecedents of the tourists’ use of these technologies is considered to be crucial for organization managers and destination policy makers. This use of social media technologies determines which elements of the trip might be used by the tourist thus having a great impact on the market. The model and its hypotheses have been tested by means of an approach based on structural equations with the PLS technique. The study was conducted on a sample of 404 individuals who normally use the Internet and had traveled on vacation in the previous 12 months. The conclusions of the study reveal that the intentions to use social media are directly influenced by the perceived benefits of that use (functional, psychological and hedonic and social); however, the costs do not significantly affect the predisposition to use such technologies. It is also shown that there is a series of incentives such as altruism, availability, individual predisposition or trust in the contributions of others which facilitate and promote the use of this type of technology when organizing and taking tourist trips. 2010 Elsevier Ltd. All rights reserved.
Realistic image composite with best-buddy prior of natural image patches
(2017)

Realistic image composite requires the appearance of foreground and background layers to be consistent. This is difficult to achieve because the foreground and the background may be taken from very different environments. This paper proposes a novel composite adjustment method that can harmonize appearance of different composite layers. We introduce the Best-Buddy Prior (BBP), which is a novel compact representations of the joint co-occurrence distribution of natural image patches. BBP can be learned from unlabelled images given only the unsupervised regional segmentation. The most-probable adjustment of foreground can be estimated efficiently in the BBP space as the shift vector to the local maximum of density function. Both qualitative and quantitative evaluations show that our method outperforms previous composite adjustment methods.
Leveraging microservices architecture by using Docker technology
(2016)

Microservices architecture is not a hype and for awhile, started getting attention from organizations who want to shorten time to market of a software product by improving productivity effect through maximizing the automation in all life circle of the product. However, microservices architecture approach also introduces a lot of new complexity and requires application developers a certain level of maturity in order to confidently apply the architectural style. Docker has been a disruptive technology which changes the way applications are being developed and distributed. With a lot of advantages, Docker is a very good fit to implementing microservices architecture. In this paper we will discuss about how Docker can effectively help in leveraging mircoservices architecture with a real working model as a case study.
Distributed Denial of Service (DDoS) Attack in Cloud- Assisted Wireless Body Area Networks: A Systematic Literature Review
(2014)

Wireless Body Area Networks (WBANs) have emerged as a promising technology that has shown enormous potential in improving the quality of healthcare, and has thus found a broad range of medical applications from ubiquitous health monitoring to emergency medical response systems. The huge amount of highly sensitive data collected and generated by WBAN nodes requires an ascendable and secure storage and processing infrastructure. Given the limited resources of WBAN nodes for storage and processing, the integration of WBANs and cloud computing may provide a powerful solution. However, despite the benefits of cloud-assisted WBAN, several security issues and challenges remain. Among these, data availability is the most nagging security issue. The most serious threat to data availability is a distributed denial of service (DDoS) attack that directly affects the all-time availability of a patient’s data. The existing solutions for standalone WBANs and sensor networks are not applicable in the cloud. The purpose of this review paper is to identify the most threatening types of DDoS attacks affecting the availability of a cloud-assisted WBAN and review the state-of-the-art detection mechanisms for the identified DDoS attacks.
60GHz high-gain low-noise amplifiers with a common-gate inductive feedback in 65nm CMOS
(2011)

In this paper, a novel design technique of common-gate inductive feedback is presented for millimeter-wave low-noise amplifiers (LNAs). For this technique, by adopting a gate inductor at the common-gate transistor of the cascode stage, the gain of the LNA can be enhanced even under a wideband operation. Using a 65nm CMOS process, transmission-line-based and spiral-inductor-based LNAs are fabricated for demonstration. With a dc power consumption of 33.6 mW from a 1.2-V supply voltage, the transmission-line-based LNA exhibits a gain of 20.6 dB and a noise figure of 5.4 dB at 60 GHz while the 3dB bandwidth is 14.1 GHz. As for the spiral-inductor-based LNA, consuming a dc power of 28.8 mW from a 1.2-V supply voltage, the circuit shows a gain of 18.0 dB and a noise figure of 4.5 dB at 60 GHz while the 3dB bandwidth is 12.2 GHz.
COLOR EYE FUNDUS IMAGES
(2013)

This paper presents an approach for automatic detection of microaneurysms and hemorrhages in fundus images. These lesions are considered the earliest signs of diabetic retinopathy. The diabetic retinopathy is a disease caused by diabetes and is considered as the major cause of blindness in working age population. The proposed method is based on mathematical morphology and consists in removing components of retinal anatomy to reach the lesions. This method consists of five stages: a) pre-processing; b) enhancement of low intensity structures; c) detection of blood vessels; d) elimination of blood vessels; e) elimination of the fovea. The accuracy of the method was tested on a public database of fundus images, where it achieved satisfactory results, comparable to other methods from the literature, reporting 87.69% and 92.44% of mean sensitivity and specificity, respectively.
Smartwatch in vivo
(2016)

In recent years, the smartwatch has returned as a form factor for mobile computing with some success. Yet it is not clear how smartwatches are used and integrated into everyday life differently from mobile phones. For this paper, we used wearable cameras to record twelve participants' daily use of smartwatches, collecting and analysing incidents where watches were used from over 34 days of user recording. This allows us to analyse in detail 1009 watch uses. Using the watch as a timepiece was the most common use, making up 50% of interactions, but only 14% of total watch usage time. The videos also let us examine why and how smartwatches are used for activity tracking, notifications, and in combination with smartphones. In discussion, we return to a key question in the study of mobile devices: how are smartwatches integrated into everyday life, in both the actions that we take and the social interactions we are part of?
Design of an Assistive Communication Glove Using Combined Sensory Channels
(2012)

This paper presents a new design of a wireless sensor glove developed for American Sign Language finger spelling gesture recognition. Five contact sensors are installed on the glove, in addition to five flex sensors on the fingers and a 3D accelerometer on the back of the hand. Each pair of flex and contact sensors are combined into the same input channel on the BSN node in order to save the number of channels and the installation area. After which, the signal is analyzed and separated back into flex and contact features by software. With electrical contacts and wirings made of conductive fabric and threads, the glove design has become thinner and more flexible. For validation, ASL finger spelling gesture recognition experiments have been performed on signals collected from six speech-impaired subjects and a normal subject. With the new sensor glove design, the experimental results have shown a significant increase in classification accuracy.
Sex for fun: a synthesis of human and animal neurobiology
(2012)

Sex is a fundamental pleasure, and crucial to the survival of our species. Though not many people would disagree with the proposition that sexual behaviour depends on the brain, the neuroscientific study of human sex is still relatively taboo and much remains to be discovered. On the contrary, excellent experimental animal models (mostly rat) are available that have uncovered major behavioural, neurochemical, and neuroanatomical characteristics of sexual behaviour. Restructuring sexual behaviour into broader terms reflecting behavioural states (wanting, liking, and inhibition) facilitates species comparison, revealing many similarities between animal and human sexual pleasure cycles, some of which can serve as potential avenues of new human sex research. In particular, behavioural and brain evidence clearly shows that motivational and consummatory phases are fundamentally distinct, and that genitally-induced sexual reward is a major factor in sexual learning mechanisms.
Distributed Method of Multiplier for Coupled Lagrangian Problems: A Control Approach
(2018)

In this paper, we propose a method for solving the distributed optimization problem in which the objective function is the sum of separable convex functions with linear constraints. In our approach, the primal variable is partially updated to make the Method of Multiplier algorithm distributed which is based on the suitable scaling of constraint matrix. The algorithm is then viewed as a dynamical system the convergence analysis of which is done using the passivity concepts of nonlinear control theory. The convexity of the function is related to the passivity of the non-linear functions which is in feedback with the positive real linear system.
SPDT Switch Using Both nMOS and pMOS Transistors for Improving Power Handling
(2015)

An SPDT switch consisting of both nMOS and pMOS transistors is presented. Compared with conventional SPDT switches using only nMOS transistors under the same bias condition, the proposed switch exhibits better power-handling capability (PHC). The mechanism for the PHC improvement is explained. A prototype is implemented using a 0.18-um CMOS process. Measurement results show that, at 2.4 GHz, the insertion loss is 0.62 dB when the nMOS transistors are on and 0.91 dB when the pMOS transistors are on. For both modes, the measured return loss and isolation are better than 10 dB and 19 dB, respectively, up to 6 GHz. Under 1.8-V operation, the switch is able to handle a 26.1-dBm input power when the nMOS transistors are on and a 24.0-dBm input power when the pMOS transistors are on.
Brain Connectivity Analysis Methods for Better Understanding of Coupling
(2012)

Action, cognition, emotion and perception can be mapped in the brain by using set of techniques. Translating unimodal concepts from one modality to another is an important step towards understanding the neural mechanisms. This paper provides a comprehensive survey of multimodal analysis of brain signals such as fMRI, EEG, MEG, NIRS and motivations, assumptions and pitfalls associated with it. All these non-invasive brain modalities complement and restrain each other and hence improve our understating of functional and neuronal organization. By combining the various modalities together, we can exploit the strengths and flaws of individual brain imaging methods. Integrated anatomical analysis and functional measurements of human brain offer a powerful paradigm for the brain mapping. Here we provide the brief review on non invasive brain modalities, describe the future of co-analysis of these
THE SERIAL POSITION EFFECT OF FREE RECALL
(2003)

Recently Murdock (1960) has shown that in free recall RI, the total number of words recalled after one presentation, is a linear function of t, total presentation time. Nothing was said about the serial position effect, though this is a well-known phenomenon of free recall (e.g., Deese & Kaufman, 1957). However, given that there is a serial position effect, the simple linear relationship between RI and t is rather surprising. In the customary serial position curve of free recall, probability of recall is plotted as a function of serial position. This means, then, that the area under the serial position curve is equal to RI, the number of words recalled after one presentation. If R j is a linear function of t then it must follow that the area under the serial position curve is also a linear function of t. However, it is not immediately apparent how the serial position curve varies with t in such a way as to maintain this simple linear relationship. The present experiment was designed as an attempt to determine how the serial position curve varied with list length and presentation rate while still maintaining this linear relationship. Unfortunately, at the end of the experiment it was still not clear how this relationship came about or, for that matter, whether the relationship was even linear after all. The basic reason for this failure was